Antibody treatments may be able to activate the immune system to fight diseases like Parkinson’s, Alzheimer’s and colorectal cancer, but they are less effective when they bind with themselves and other molecules that aren’t markers of disease.
Now, new machine-learning algorithms developed at the University of Michigan can highlight problem areas in antibodies that make them prone to binding non-target molecules.
“We can use the models to pinpoint the positions in antibodies that are causing trouble and change those positions to correct the problem without causing new ones,” said Peter Tessier, the Albert M. Mattocks Professor of Pharmaceutical Sciences at U-M and corresponding author of the study in Nature Biomedical Engineering… Continue reading.
WASHINGTON, D.C.—The American Institute for Medical and Biological Engineering (AIMBE) has announced the induction of Peter M. Tessier, Ph.D., Albert M. Mattocks Professor of Pharmaceutical Sciences and Chemical Engineering, Departments of Pharmaceutical Sciences, Chemical Engineering and Biomedical Engineering, University of Michigan, to its College of Fellows. Dr. Tessier was nominated, reviewed, and elected by peers and members of the College of Fellows for outstanding contributions in the design, engineering and selection of monoclonal antibodies for therapeutic applications.